Chances are, you’re about to take your first steps towards digital transformation. This is a big decision and therefore you have researched it thoroughly. We assume you have identified the need of having a data science team, and if so there’s something we need to tell you:
Simply Having a Data Science Team Does Not Guarantee Success
Such becomes clear if we look at the statistics: 80% of analytics projects result in failure. There are multiple reasons behind this number, from unsuccessful embedment of the team into business to jumping into action without a clear strategy.
In this article we will explore what areas need to be addressed in order for your data science team to drive digital transformation enabling results.
Digital transformation is currently trending and everyone is in a rush to be early adopters. Often it’s a hasty decision that is quickly accelerated into targets and ways of achieving them. Not outlining WHY does your business need digital transformation is a big yet common mistake. We have designed a tool to evaluate your readiness to avoid making it.
To put it simply, ‘why’ is what drives the action. If there’s no defined purpose of starting a project, it will be impossible to set clear goals. Goal is the cornerstone of a strategy, and so if it’s not clearly stated, the strategy won’t be effective. In other words, without a ‘why’, the ‘what’ and ‘how’ are impossible to answer and your project will fail.
Defining the reason behind starting a project and evaluating their outcome on how they contribute to business goals takes effort. However, doing that gives a clear indication of what the objectives are, ultimately leading to clearer analyses and results.
A few blog posts ago we have spoken about roles that are vital for a high functioning data science team. However, you should use that as a guide for positions to fill as opposed to a team structure template. Every business has specific goals, company culture, organisational structure: and there is no one-for-all solution when it comes to structuring a data science team.
While no fully-functional data science team will consist of data scientists only, there are instances in which it has to be heavily data scientist-focused. For example, tech companies that build data products will prioritise programming and data engineering skills. Businesses that are seeking to use data to leverage their performance, although still requiring programming skills, will expect higher levels of commercial awareness. With that said, roles that deliver a solution are still needed in both scenarios.
The point is that you need to build a team that will be capable of meeting specific requirements your business has and therefore it’s up to you how you’re going to structure it. This, however, doesn’t mean you should be excluding roles with different skill sets entirely.
This may seem obvious, but prioritising projects is crucial. Things don’t always go according to plan and when such issues arise, clearly outline priorities allow aligning projects back into efficiency-driving strategies. The key issue that arises when prioritising projects is estimating their value. While factors like urgency and importance are relatively easy to determine, they can shift based on paybacks and ROI received. For this reason, we have developed a prioritisation tool that makes this process easier and less confusing.
When prioritised, strategic projects have a higher success rate as they clear doubts upon being forced to make decisions. They also nurture a data-driven culture when reasons behind prioritising certain projects over others is clearly communicated to all the departments. Overall, although it’s a concept that’s often taken for granted, prioritisation is at the core of aligning and focusing management around goals.
Implementation of a framework to answer the ‘why’ and finding an effective process that works with the team and aligns to business goals sounds too basic to be spoken about. However, 80% of data scientists have admitted to only ‘just kind of doing it’.
This is why the role of the data science manager as discussed in a previous blog is so important. Following a process is something that takes time to organise through some trial and error, but without a good data science manager, projects end up getting shelved and the value of the data science team never gets realised.
Due to neglecting something simple, many opportunities are being missed. Lack of definite effective processes in turn leads to poorer quality of insights, increased risks and reduced productivity. There is no answer to what kind of project processes should be implemented either – it all depends on your team and what challenges you’re trying to overcome.
The success behind digital transformation is largely dependent on the mindset. Normally, data scientists only actively communicate with senior business leaders, but not the rest of the organisation. Other departments tend to be sceptical when urged to change their processes based on recommendations of the data science team. Such issues arise due to lack of communication and therefore misalignment on goals and objectives.
As we have discussed before, successful embedment of the data science team into workflows of every department builds understanding between them. In turn this creates and nurtures a data-driven culture, where every employee understands the impact of their action towards the bigger picture as opposed to personal or department targets.
Finally, to be truly impactful the data science team needs to assess performance of processes they’ve put in place. Performance assessment brings out weaknesses and how they could be improved, enabling further optimisation and efficiency.
The core of machine learning is about learning from experience. Good leaders should do the same. If the approach of track, measure, evaluate and update works for other areas of the business, it will work for your data science team too. Having a long term plan to build capability and adopting a learning and improvement mindset will keep your data science team on the cutting edge for longer.
Comparing the results achieved against overall goals is also a good way to measure project success, which in turn may indicate need for changes. Sometimes this may require tinkering with the strategy, sometimes – rethinking the structure of the team.
Building a data science team won’t bring instant success. In fact, unless you’re capable of outlining what its role in your company is, your projects are bound to fail. Every business has different needs, therefore different reasons behind wanting digital transformation.
There is one thing in common, though: data science does not deliver digital transformation, it aids your company’s journey towards achieving it.
Need further guidance? Let us know.